This article is part of the Forecasting series, split from the original Forecasting article.

The ML forecast is then combined with the volume anticipated due to Lifecycle events, to establish a starting point for the Demand Plan.

At the most detailed level, we are trying to forecast demand at the Item/Location/Customer level. However, the data at this level can be sparse and forecasts are inaccurate. 

In general, the higher the level of aggregation the better the accuracy but aggregate forecasts still must be converted to a more detailed level for collaboration and operational planning. For example, for budgeting purposes, it may be sufficient to predict the overall increase in sales. However, for planning production or procurement orders, it is necessary to predict the requirements by item and location. 

The Pyramid Forecast uses machine learning techniques to automatically develop an aggregation strategy that will both maximize accuracy and provide a useful forecast. When comparing different aggregation strategies, the algorithms evaluate the accuracy of the resulting forecast at the “Item” level derived from the updated forecast at the item, location and customer level.  

In the following example, the best forecast strategy is a two-level pyramid:

The data is first aggregated by Item and Location to generate a forecast. It is then disaggregated to the Item/Location/Customer level using the detailed customer information. 

The accuracy numbers measure how well the forecast at each level performs with respect to history at the “Item” level. The machine learning algorithm considers all possible aggregations of data by the attributes that the user specifies. A pyramid level is added only if the addition will improve the forecast accuracy at the “Item” level.

In our example, adding the Item/Location aggregation reduces the forecast error at the Item level from 29% to 26%.